Help on class TrajectoryCollectionAggregator in module movingpandas.trajectory_aggregator:
class TrajectoryCollectionAggregator(builtins.object)
| TrajectoryCollectionAggregator(traj_collection, max_distance, min_distance, min_stop_duration, min_angle=45)
|
| Methods defined here:
|
| __init__(self, traj_collection, max_distance, min_distance, min_stop_duration, min_angle=45)
| Aggregates trajectories by extracting significant points, clustering those points, and extracting
| flows between clusters.
|
| Based on the algorithm described by Andrienko, N., & Andrienko, G. (2011). Spatial generalization and
| aggregation of massive movement data. IEEE Transactions on visualization and computer graphics, 17(2), 205-219.
|
| Parameters
| ----------
| traj_collection : TrajectoryCollection
| TrajectoryCollection to be aggregated
| max_distance : float
| Maximum distance between significant points (distance is calculated in CRS units, except if the CRS
| is geographic, e.g. EPSG:4326 WGS84, then distance is calculated in meters)
| min_distance : float
| Minimum distance between significant points
| min_stop_duration : integer
| Minimum duration required for stop detection (in seconds)
| min_angle : float
| Minimum angle for significant point extraction
|
| get_clusters_gdf(self)
| Return the extracted cluster centroids
|
| Returns
| -------
| GeoDataFrame
| Cluster centroids, incl. the number of clustered significant points (n)
|
| get_flows_gdf(self)
| Return the extracted flows
|
| Returns
| -------
| GeoDataFrame
| Flow lines, incl. the number of trajectories summarized in the flow (weight)
|
| get_significant_points_gdf(self)
| Return the extracted significant points
|
| Returns
| -------
| GeoDataFrame
| Significant points
|
| ----------------------------------------------------------------------
| Data descriptors defined here:
|
| __dict__
| dictionary for instance variables (if defined)
|
| __weakref__
| list of weak references to the object (if defined)
